多传感器交叉提示多目标探测动态联盟技术研究

针对多传感器交叉提示多目标连续、高概率探测问题，引入动态联盟思想，提出了多传感器交叉提示多目标探测的动态联盟机制，为解决被提示传感器的选择问题建立了动态联盟的数学模型，提出了对每个目标形成一个动态联盟进行探测求解多个传感器动态联盟的带变异算子的离散粒子群算法。仿真和实验结果表明该算法能够得到更满意的解，优化了系统综合探测概率、总消耗以及所有目标联盟成员总数三种性能，与实际情形要求一致，从而表明了模型的合理性和算法的有效性。

Research on Technique of Multi\|Target Detection Using Multi\|Sensor Cross\|Cueing Based on Dynamic Coalition

To study the continual and high probability detection for multiple targets using multi\|sensor cross\|cueing, a dynamic coalition scheme and a mathematic model are proposed for the multi\|target detection using multi\|sensor cross\|cueing. Furthermore, by forming a dynamic coalition for detecting every target, an improved discrete particle swarm optimization algorithm with mutation operator is designed to solve multiple sensor dynamic coalition problems in multi\|target detection. The simulation and experimental results show that the algorithm can attain better performances than the algorithm without the mutation operator and it optimizes the performances among the integrated detection probability, the total consumption and the total number of coalition members for all targets, and the rationality of model and the effectiveness of algorithm have been demonstrated.